You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
308 lines
11 KiB
308 lines
11 KiB
|
|
#include <string.h>
|
|
#include <assert.h>
|
|
#include <fcntl.h>
|
|
#include <unistd.h>
|
|
#include <eigen3/Eigen/Dense>
|
|
|
|
#include "common/timing.h"
|
|
#include "common/params.h"
|
|
#include "driving.h"
|
|
#include "clutil.h"
|
|
|
|
constexpr int DESIRE_PRED_SIZE = 32;
|
|
constexpr int OTHER_META_SIZE = 4;
|
|
|
|
constexpr int MODEL_WIDTH = 512;
|
|
constexpr int MODEL_HEIGHT = 256;
|
|
constexpr int MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT * 3 / 2;
|
|
|
|
constexpr int PLAN_MHP_N = 5;
|
|
constexpr int PLAN_MHP_COLUMNS = 30;
|
|
constexpr int PLAN_MHP_VALS = 30*33;
|
|
constexpr int PLAN_MHP_SELECTION = 1;
|
|
constexpr int PLAN_MHP_GROUP_SIZE = (2*PLAN_MHP_VALS + PLAN_MHP_SELECTION);
|
|
|
|
constexpr int LEAD_MHP_N = 5;
|
|
constexpr int LEAD_MHP_VALS = 4;
|
|
constexpr int LEAD_MHP_SELECTION = 3;
|
|
constexpr int LEAD_MHP_GROUP_SIZE = (2*LEAD_MHP_VALS + LEAD_MHP_SELECTION);
|
|
|
|
constexpr int POSE_SIZE = 12;
|
|
|
|
constexpr int PLAN_IDX = 0;
|
|
constexpr int LL_IDX = PLAN_IDX + PLAN_MHP_N*PLAN_MHP_GROUP_SIZE;
|
|
constexpr int LL_PROB_IDX = LL_IDX + 4*2*2*33;
|
|
constexpr int RE_IDX = LL_PROB_IDX + 4;
|
|
constexpr int LEAD_IDX = RE_IDX + 2*2*2*33;
|
|
constexpr int LEAD_PROB_IDX = LEAD_IDX + LEAD_MHP_N*(LEAD_MHP_GROUP_SIZE);
|
|
constexpr int DESIRE_STATE_IDX = LEAD_PROB_IDX + 3;
|
|
constexpr int META_IDX = DESIRE_STATE_IDX + DESIRE_LEN;
|
|
constexpr int POSE_IDX = META_IDX + OTHER_META_SIZE + DESIRE_PRED_SIZE;
|
|
constexpr int OUTPUT_SIZE = POSE_IDX + POSE_SIZE;
|
|
#ifdef TEMPORAL
|
|
constexpr int TEMPORAL_SIZE = 512;
|
|
#else
|
|
constexpr int TEMPORAL_SIZE = 0;
|
|
#endif
|
|
|
|
// #define DUMP_YUV
|
|
|
|
void model_init(ModelState* s, cl_device_id device_id, cl_context context) {
|
|
frame_init(&s->frame, MODEL_WIDTH, MODEL_HEIGHT, device_id, context);
|
|
s->input_frames = std::make_unique<float[]>(MODEL_FRAME_SIZE * 2);
|
|
|
|
constexpr int output_size = OUTPUT_SIZE + TEMPORAL_SIZE;
|
|
s->output.resize(output_size);
|
|
|
|
#if defined(QCOM) || defined(QCOM2)
|
|
s->m = std::make_unique<ThneedModel>("../../models/supercombo.thneed", &s->output[0], output_size, USE_GPU_RUNTIME);
|
|
#else
|
|
s->m = std::make_unique<DefaultRunModel>("../../models/supercombo.dlc", &s->output[0], output_size, USE_GPU_RUNTIME);
|
|
#endif
|
|
|
|
#ifdef TEMPORAL
|
|
s->m->addRecurrent(&s->output[OUTPUT_SIZE], TEMPORAL_SIZE);
|
|
#endif
|
|
|
|
#ifdef DESIRE
|
|
s->m->addDesire(s->pulse_desire, DESIRE_LEN);
|
|
#endif
|
|
|
|
#ifdef TRAFFIC_CONVENTION
|
|
const int idx = Params().read_db_bool("IsRHD") ? 1 : 0;
|
|
s->traffic_convention[idx] = 1.0;
|
|
s->m->addTrafficConvention(s->traffic_convention, TRAFFIC_CONVENTION_LEN);
|
|
#endif
|
|
|
|
s->q = CL_CHECK_ERR(clCreateCommandQueue(context, device_id, 0, &err));
|
|
}
|
|
|
|
ModelDataRaw model_eval_frame(ModelState* s, cl_mem yuv_cl, int width, int height,
|
|
const mat3 &transform, float *desire_in) {
|
|
#ifdef DESIRE
|
|
if (desire_in != NULL) {
|
|
for (int i = 1; i < DESIRE_LEN; i++) {
|
|
// Model decides when action is completed
|
|
// so desire input is just a pulse triggered on rising edge
|
|
if (desire_in[i] - s->prev_desire[i] > .99) {
|
|
s->pulse_desire[i] = desire_in[i];
|
|
} else {
|
|
s->pulse_desire[i] = 0.0;
|
|
}
|
|
s->prev_desire[i] = desire_in[i];
|
|
}
|
|
}
|
|
#endif
|
|
|
|
//for (int i = 0; i < OUTPUT_SIZE + TEMPORAL_SIZE; i++) { printf("%f ", s->output[i]); } printf("\n");
|
|
|
|
float *new_frame_buf = frame_prepare(&s->frame, s->q, yuv_cl, width, height, transform);
|
|
memmove(&s->input_frames[0], &s->input_frames[MODEL_FRAME_SIZE], sizeof(float)*MODEL_FRAME_SIZE);
|
|
memmove(&s->input_frames[MODEL_FRAME_SIZE], new_frame_buf, sizeof(float)*MODEL_FRAME_SIZE);
|
|
s->m->execute(&s->input_frames[0], MODEL_FRAME_SIZE*2);
|
|
|
|
#ifdef DUMP_YUV
|
|
FILE *dump_yuv_file = fopen("/sdcard/dump.yuv", "wb");
|
|
fwrite(new_frame_buf, MODEL_HEIGHT*MODEL_WIDTH*3/2, sizeof(float), dump_yuv_file);
|
|
fclose(dump_yuv_file);
|
|
assert(1==2);
|
|
#endif
|
|
|
|
clEnqueueUnmapMemObject(s->q, s->frame.net_input, (void*)new_frame_buf, 0, NULL, NULL);
|
|
|
|
// net outputs
|
|
ModelDataRaw net_outputs;
|
|
net_outputs.plan = &s->output[PLAN_IDX];
|
|
net_outputs.lane_lines = &s->output[LL_IDX];
|
|
net_outputs.lane_lines_prob = &s->output[LL_PROB_IDX];
|
|
net_outputs.road_edges = &s->output[RE_IDX];
|
|
net_outputs.lead = &s->output[LEAD_IDX];
|
|
net_outputs.lead_prob = &s->output[LEAD_PROB_IDX];
|
|
net_outputs.meta = &s->output[DESIRE_STATE_IDX];
|
|
net_outputs.pose = &s->output[POSE_IDX];
|
|
return net_outputs;
|
|
}
|
|
|
|
void model_free(ModelState* s) {
|
|
frame_free(&s->frame);
|
|
CL_CHECK(clReleaseCommandQueue(s->q));
|
|
}
|
|
|
|
static const float *get_best_data(const float *data, int size, int group_size, int offset) {
|
|
int max_idx = 0;
|
|
for (int i = 1; i < size; i++) {
|
|
if (data[(i + 1) * group_size + offset] >
|
|
data[(max_idx + 1) * group_size + offset]) {
|
|
max_idx = i;
|
|
}
|
|
}
|
|
return &data[max_idx * group_size];
|
|
}
|
|
|
|
static const float *get_plan_data(float *plan) {
|
|
return get_best_data(plan, PLAN_MHP_N, PLAN_MHP_GROUP_SIZE, -1);
|
|
}
|
|
|
|
static const float *get_lead_data(const float *lead, int t_offset) {
|
|
return get_best_data(lead, LEAD_MHP_N, LEAD_MHP_GROUP_SIZE, t_offset - LEAD_MHP_SELECTION);
|
|
}
|
|
|
|
|
|
void fill_lead_v2(cereal::ModelDataV2::LeadDataV2::Builder lead, const float *lead_data, const float *prob, int t_offset, float t) {
|
|
const float *data = get_lead_data(lead_data, t_offset);
|
|
lead.setProb(sigmoid(prob[t_offset]));
|
|
lead.setT(t);
|
|
float xyva_arr[LEAD_MHP_VALS];
|
|
float xyva_stds_arr[LEAD_MHP_VALS];
|
|
for (int i=0; i<LEAD_MHP_VALS; i++) {
|
|
xyva_arr[i] = data[i];
|
|
xyva_stds_arr[i] = exp(data[LEAD_MHP_VALS + i]);
|
|
}
|
|
lead.setXyva(xyva_arr);
|
|
lead.setXyvaStd(xyva_stds_arr);
|
|
}
|
|
|
|
void fill_meta(cereal::ModelDataV2::MetaData::Builder meta, const float *meta_data) {
|
|
float desire_state_softmax[DESIRE_LEN];
|
|
float desire_pred_softmax[4*DESIRE_LEN];
|
|
softmax(&meta_data[0], desire_state_softmax, DESIRE_LEN);
|
|
for (int i=0; i<4; i++) {
|
|
softmax(&meta_data[DESIRE_LEN + OTHER_META_SIZE + i*DESIRE_LEN],
|
|
&desire_pred_softmax[i*DESIRE_LEN], DESIRE_LEN);
|
|
}
|
|
meta.setDesireState(desire_state_softmax);
|
|
meta.setEngagedProb(sigmoid(meta_data[DESIRE_LEN]));
|
|
meta.setGasDisengageProb(sigmoid(meta_data[DESIRE_LEN + 1]));
|
|
meta.setBrakeDisengageProb(sigmoid(meta_data[DESIRE_LEN + 2]));
|
|
meta.setSteerOverrideProb(sigmoid(meta_data[DESIRE_LEN + 3]));
|
|
meta.setDesirePrediction(desire_pred_softmax);
|
|
}
|
|
|
|
void fill_xyzt(cereal::ModelDataV2::XYZTData::Builder xyzt, const float * data,
|
|
int columns, int column_offset, float * plan_t_arr) {
|
|
float x_arr[TRAJECTORY_SIZE] = {};
|
|
float y_arr[TRAJECTORY_SIZE] = {};
|
|
float z_arr[TRAJECTORY_SIZE] = {};
|
|
//float x_std_arr[TRAJECTORY_SIZE];
|
|
//float y_std_arr[TRAJECTORY_SIZE];
|
|
//float z_std_arr[TRAJECTORY_SIZE];
|
|
float t_arr[TRAJECTORY_SIZE];
|
|
for (int i=0; i<TRAJECTORY_SIZE; i++) {
|
|
// column_offset == -1 means this data is X indexed not T indexed
|
|
if (column_offset >= 0) {
|
|
t_arr[i] = T_IDXS[i];
|
|
x_arr[i] = data[i*columns + 0 + column_offset];
|
|
//x_std_arr[i] = data[columns*(TRAJECTORY_SIZE + i) + 0 + column_offset];
|
|
} else {
|
|
t_arr[i] = plan_t_arr[i];
|
|
x_arr[i] = X_IDXS[i];
|
|
//x_std_arr[i] = NAN;
|
|
}
|
|
y_arr[i] = data[i*columns + 1 + column_offset];
|
|
//y_std_arr[i] = data[columns*(TRAJECTORY_SIZE + i) + 1 + column_offset];
|
|
z_arr[i] = data[i*columns + 2 + column_offset];
|
|
//z_std_arr[i] = data[columns*(TRAJECTORY_SIZE + i) + 2 + column_offset];
|
|
}
|
|
//kj::ArrayPtr<const float> x_std(x_std_arr, TRAJECTORY_SIZE);
|
|
//kj::ArrayPtr<const float> y_std(y_std_arr, TRAJECTORY_SIZE);
|
|
//kj::ArrayPtr<const float> z_std(z_std_arr, TRAJECTORY_SIZE);
|
|
xyzt.setX(x_arr);
|
|
xyzt.setY(y_arr);
|
|
xyzt.setZ(z_arr);
|
|
//xyzt.setXStd(x_std);
|
|
//xyzt.setYStd(y_std);
|
|
//xyzt.setZStd(z_std);
|
|
xyzt.setT(t_arr);
|
|
}
|
|
|
|
void fill_model(cereal::ModelDataV2::Builder &framed, const ModelDataRaw &net_outputs) {
|
|
// plan
|
|
const float *best_plan = get_plan_data(net_outputs.plan);
|
|
float plan_t_arr[TRAJECTORY_SIZE];
|
|
for (int i=0; i<TRAJECTORY_SIZE; i++) {
|
|
plan_t_arr[i] = best_plan[i*PLAN_MHP_COLUMNS + 15];
|
|
}
|
|
|
|
fill_xyzt(framed.initPosition(), best_plan, PLAN_MHP_COLUMNS, 0, plan_t_arr);
|
|
fill_xyzt(framed.initVelocity(), best_plan, PLAN_MHP_COLUMNS, 3, plan_t_arr);
|
|
fill_xyzt(framed.initOrientation(), best_plan, PLAN_MHP_COLUMNS, 9, plan_t_arr);
|
|
fill_xyzt(framed.initOrientationRate(), best_plan, PLAN_MHP_COLUMNS, 12, plan_t_arr);
|
|
|
|
// lane lines
|
|
auto lane_lines = framed.initLaneLines(4);
|
|
float lane_line_probs_arr[4];
|
|
float lane_line_stds_arr[4];
|
|
for (int i = 0; i < 4; i++) {
|
|
fill_xyzt(lane_lines[i], &net_outputs.lane_lines[i*TRAJECTORY_SIZE*2], 2, -1, plan_t_arr);
|
|
lane_line_probs_arr[i] = sigmoid(net_outputs.lane_lines_prob[i]);
|
|
lane_line_stds_arr[i] = exp(net_outputs.lane_lines[2*TRAJECTORY_SIZE*(4 + i)]);
|
|
}
|
|
framed.setLaneLineProbs(lane_line_probs_arr);
|
|
framed.setLaneLineStds(lane_line_stds_arr);
|
|
|
|
// road edges
|
|
auto road_edges = framed.initRoadEdges(2);
|
|
float road_edge_stds_arr[2];
|
|
for (int i = 0; i < 2; i++) {
|
|
fill_xyzt(road_edges[i], &net_outputs.road_edges[i*TRAJECTORY_SIZE*2], 2, -1, plan_t_arr);
|
|
road_edge_stds_arr[i] = exp(net_outputs.road_edges[2*TRAJECTORY_SIZE*(2 + i)]);
|
|
}
|
|
framed.setRoadEdgeStds(road_edge_stds_arr);
|
|
|
|
// meta
|
|
fill_meta(framed.initMeta(), net_outputs.meta);
|
|
|
|
// leads
|
|
auto leads = framed.initLeads(LEAD_MHP_SELECTION);
|
|
float t_offsets[LEAD_MHP_SELECTION] = {0.0, 2.0, 4.0};
|
|
for (int t_offset=0; t_offset<LEAD_MHP_SELECTION; t_offset++) {
|
|
fill_lead_v2(leads[t_offset], net_outputs.lead, net_outputs.lead_prob, t_offset, t_offsets[t_offset]);
|
|
}
|
|
}
|
|
|
|
void model_publish(PubMaster &pm, uint32_t vipc_frame_id, uint32_t frame_id, float frame_drop,
|
|
const ModelDataRaw &net_outputs, uint64_t timestamp_eof,
|
|
float model_execution_time, kj::ArrayPtr<const float> raw_pred) {
|
|
const uint32_t frame_age = (frame_id > vipc_frame_id) ? (frame_id - vipc_frame_id) : 0;
|
|
MessageBuilder msg;
|
|
auto framed = msg.initEvent().initModelV2();
|
|
framed.setFrameId(vipc_frame_id);
|
|
framed.setFrameAge(frame_age);
|
|
framed.setFrameDropPerc(frame_drop * 100);
|
|
framed.setTimestampEof(timestamp_eof);
|
|
framed.setModelExecutionTime(model_execution_time);
|
|
if (send_raw_pred) {
|
|
framed.setRawPredictions(raw_pred.asBytes());
|
|
}
|
|
fill_model(framed, net_outputs);
|
|
pm.send("modelV2", msg);
|
|
}
|
|
|
|
void posenet_publish(PubMaster &pm, uint32_t vipc_frame_id, uint32_t vipc_dropped_frames,
|
|
const ModelDataRaw &net_outputs, uint64_t timestamp_eof) {
|
|
float trans_arr[3];
|
|
float trans_std_arr[3];
|
|
float rot_arr[3];
|
|
float rot_std_arr[3];
|
|
|
|
for (int i =0; i < 3; i++) {
|
|
trans_arr[i] = net_outputs.pose[i];
|
|
trans_std_arr[i] = exp(net_outputs.pose[6 + i]);
|
|
|
|
rot_arr[i] = net_outputs.pose[3 + i];
|
|
rot_std_arr[i] = exp(net_outputs.pose[9 + i]);
|
|
}
|
|
|
|
MessageBuilder msg;
|
|
auto posenetd = msg.initEvent(vipc_dropped_frames < 1).initCameraOdometry();
|
|
posenetd.setTrans(trans_arr);
|
|
posenetd.setRot(rot_arr);
|
|
posenetd.setTransStd(trans_std_arr);
|
|
posenetd.setRotStd(rot_std_arr);
|
|
|
|
posenetd.setTimestampEof(timestamp_eof);
|
|
posenetd.setFrameId(vipc_frame_id);
|
|
|
|
pm.send("cameraOdometry", msg);
|
|
}
|
|
|